23 papers with code • 1 benchmarks • 1 datasets
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.
Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
Labeled datasets for semantic segmentation are imperfect, especially in medical imaging where borders are often subtle or ill-defined.
In this paper, we present a novel semi-supervised method for medical image segmentation, where the network is optimized by the weighted combination of a common supervised loss for labeled inputs only and a regularization loss for both labeled and unlabeled data.
Manually tracing regions of interest (ROIs) within the liver is the de facto standard method for measuring liver attenuation on computed tomography (CT) in diagnosing nonalcoholic fatty liver disease (NAFLD).
Generating large labeled data sets for laparoscopic image processing tasks using unpaired image-to-image translation
We show that this data set can be used to train models for the task of liver segmentation of laparoscopic images.
Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation
In this study, the optimal input configuration of DCE MR images for convolutional neural networks (CNNs) is studied.